cor2cov: Converting a correlation matrix into a covariance matrix

In propagate: Propagation of Uncertainty

Converting a correlation matrix into a covariance matrix

Description

Converts a correlation matrix into a covariance matrix using variance information. It is therefore the opposite of cov2cor.

Usage

cor2cov(C, var)

Arguments

C	a symmetric numeric correlation matrix \mathbf{C}.
var	a vector of variances \sigma_n^2.

Details

Calculates the covariance matrix \mathbf{\Sigma} using a correlation matrix \mathbf{C} and outer products of the standard deviations \sigma_n:

\mathbf{\Sigma} = \mathbf{C} \cdot \sigma_n \otimes \sigma_n

Value

The corresponding covariance matrix.

Author(s)

Andrej-Nikolai Spiess

Examples

## Example in Annex H.2 from the GUM 2008 manual
## (see 'References'), simultaneous resistance
## and reactance measurement.
data(H.2)
attach(H.2)

## Original covariance matrix.
COV <- cov(H.2)
## extract variances
VAR <- diag(COV)

## cor2cov covariance matrix.
COV2 <- cor2cov(cor(H.2), VAR) 

## Equal to original covariance matrix.
all.equal(COV2, COV)

propagate documentation built on June 8, 2025, 9:59 a.m.